Created by W.Langdon from gp-bibliography.bib Revision:1.4868

- @InProceedings{Yim:2012:ICCAS,
- author = "Hyungu Yim and DaeEun Kim",
- booktitle = "12th International Conference on Control, Automation and Systems (ICCAS 2012)",
- title = "Evolving internal memory strategies for the woods problems",
- year = "2012",
- pages = "366--369",
- keywords = "genetic algorithms, genetic programming, finite state machines, genetic algorithms, mobile robots, GP-automata controllers, behaviour performance, finite state automata, finite state machine, hidden state problems, internal memory strategies, memory states, mobile robots, perceptual aliasing problems, purely reactive systems, robotics researches, sensor states, woods problems, Automata, Biological cells, Educational institutions, Evolutionary computation, Position measurement, Robot sensing systems, Evolutionary computation, Finite State Machine, GP-automata, Perceptual aliasing, Woods Problem",
- URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6393463",
- size = "4 pages",
- abstract = "Purely reactive systems have been used in many robotics researches. However, they have difficulty in solving the hidden state problems. Internal memory has been used to solve the hidden state problems, which is also called the perceptual aliasing problems. Woods problem is one of the perceptual aliasing problems. In this paper, we apply two methods, Finite State Machine and GP-automata controllers, to solve the Woods problem. These two methods are compared in terms of the behaviour performance of the agents with internal memory and sensor states. The performance of each method in the Woods problem is measured by the average number of time steps needed to reach a goal position from all possible initial positions. The analysis of the memory shows that both memory states and sensor states affect the behaviour performance of the agent.",
- notes = "Also known as \cite{6393463}",
- }

Genetic Programming entries for Hyungu Yim DaeEun Kim